Discovery of functional noncoding elements by digital analysis of chromatin structure - PubMed (original) (raw)

. 2004 Nov 30;101(48):16837-42.

doi: 10.1073/pnas.0407387101. Epub 2004 Nov 18.

Michael Hawrylycz, James C Wallace, Richard Humbert, Man Yu, Anthony Shafer, Janelle Kawamoto, Robert Hall, Joshua Mack, Michael O Dorschner, Michael McArthur, John A Stamatoyannopoulos

Affiliations

Discovery of functional noncoding elements by digital analysis of chromatin structure

Peter J Sabo et al. Proc Natl Acad Sci U S A. 2004.

Abstract

We developed a quantitative methodology, digital analysis of chromatin structure (DACS), for high-throughput, automated mapping of DNase I-hypersensitive sites and associated cis-regulatory sequences in the human and other complex genomes. We used 19/20-bp genomic DNA tags to localize individual DNase I cutting events in nuclear chromatin and produced approximately 257,000 tags from erythroid cells. Tags were mapped to the human genome, and a quantitative algorithm was applied to discriminate statistically significant clusters of independent DNase I cutting events. We show that such clusters identify both known regulatory sequences and previously unrecognized functional elements across the genome. We used in silico simulation to demonstrate that DACS is capable of efficient and accurate localization of the majority of DNase I-hypersensitive sites in the human genome without requiring an independent validation step. A unique feature of DACS is that it permits unbiased evaluation of the chromatin state of regulatory sequences from widely separated genomic loci. We found surprisingly large differences in the accessibility of distant regulatory sequences, suggesting the existence of a hierarchy of nuclear organization that escapes detection by conventional chromatin assays.

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Figures

Fig. 1.

Fig. 1.

Schematic of DACS library creation. See Methods and Supporting Text for description and Fig. 6 for additional illustration.

Fig. 2.

Fig. 2.

Enrichment of DACS tags and clusters in genomic regions associated with regulation. y axes: average number of individual DACS tags or statistically significant tag clusters per 100-bp bin. x axes: normalized distance (kb) relative to genomic landmark. (A) Distribution of 154,744 distinct, uniquely mapping tags relative to TSSs (orange) and 3′ ends of ≈18,000 RefSeq genes (green). (B) Statistically significant tag clusters (green; n = 3,492) show markedly greater enrichment relative to TSSs vs. individual tags (orange).

Fig. 3.

Fig. 3.

Genomic localization of 257,443 DACS tags. (A and B) Predictive potential of clusters for HSs increases exponentially. (A) Percentage of cluster centroids within ±2 kb of an annotated RefSeq TSS as a function of cluster size (size = 1 denotes individual tags). (B) Percentage of cluster centroids that coincide precisely with DNase I HSs as a function of cluster size. The ability to correctly predict the location of the HS increases exponentially as a function of cluster size. (C and D) Previously unrecognized elements identified by DACS clusters correspond with classical DNase I HSs. Conventional DNase I hypersensitivity assays were performed to examine previously unrecognized elements identified by DACS. (C) Conventional hypersensitivity assay of the DACS-identified HS in an internal intron of LRBA (see Fig. 4_E_). (D) The HS element/DACS cluster shown in Fig. 4_F_ (parental bands: 11.1-kb _Eco_N1 and 6-kb _Hin_dIII fragments, respectively).

Fig. 4.

Fig. 4.

DACS tag clusters identify known and previously unrecognized functional elements. Tag positions (orange and yellow vertical arrows) are shown relative to chromosomal position, known genes (blue), CpG islands (green), and human–mouse conservation (brown). Statistically significant tag clusters are identified with orange arrows and horizontal brackets. (A_–_C) Examples of known regulatory elements identified de novo by DACS: promoter of erythroid-specific transcription factor NF-E2 gene (chr12) (A); TAL1/_SCL 3_′ enhancer (chr1) (26) (B); p53 promoter complex (chr17) (27) (C). Note that the computed cluster centroid (*) falls between two HSs (thick arrows). (D_–_G) Examples of previously unrecognized elements identified by DACS: element within intron of _N_-acetylgalactosaminyltransferase gene (GALNT1; chr18) (D); intronic element within lipopolysaccharide-responsive/beige-like anchor protein (LRBA; chr4) (E); cluster over CpG island 12 kb upstream of gene of unknown function on chr3 (F); cluster over highly conserved sequence block on chr2 located >100 kb from any known gene (G). Repeated tagging of specific functional elements in advance of others suggests a discrete hierarchy of nuclear chromatin organization that escapes detection with conventional assays.

Fig. 5.

Fig. 5.

Modeling genome-scale discovery of HSs with DACS. Shown are results of an in silico simulation of DACS indicating the number of HSs (y axis) that would be identified at a fixed 90% PPV threshold as a function of the number of tags (x axis) mapped for a given input tag-population enrichment for HSs (colored curves; range 2–20%). DACS was simulated against a model genome in which 50,000 model DNase I HSs were distributed against the complete human genome sequence. As expected, the number of HSs predicted with >90% accuracy grows rapidly and then levels off. Larger numbers of tags will eventually enable identification of most HSs in the population (not shown).

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